2 papers
stat.ML2026
JUCAL: Jointly Calibrating Aleatoric and Epistemic Uncertainty in Classification Tasks
Jakob Heiss, Sören Lambrecht, Jakob Weissteiner +4
We study post-calibration uncertainty for trained ensembles of classifiers. Specifically, we consider both aleatoric (label noise) and epistemic (model) uncertainty. Among the most…
q-fin.PM2023
Randomized Signature Methods in Optimal Portfolio Selection
Erdinc Akyildirim, Matteo Gambara, Josef Teichmann +1
We present convincing empirical results on the application of Randomized Signature Methods for non-linear, non-parametric drift estimation for a multi-variate financial market. Eve…